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Neither humans nor generative AI are reliably more creative in every situation. AI can produce useful ideas quickly and perform strongly on some bounded creativity tests, while people show greater variation in at least one large-scale comparison and outperform models on some tasks. The better choice depends on what the work requires, how AI enters the process, and whether a person can judge which results are meaningful and appropriate.
For most creative work, treat AI as an optional contributor—not an automatic substitute or a final authority. Use it to explore possibilities when that helps; keep human judgment central when context, values, originality, or responsibility matter.
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Is AI more creative than humans?
There is no reliable all-purpose winner. “Creativity” can refer to an output that people rate as original, the ability to generate many ideas, success on a particular problem-solving task, or the process of developing and selecting ideas. Results on one of these measures do not establish an overall ranking.
Recent evidence points in different directions because the studies ask different questions. A 2026 study of GPT-3.5-turbo, GPT-4, GPT-4o, and human participants found that the models outperformed people on the study’s divergent- and convergent-thinking tasks and insight problem-solving, but underperformed on creative writing. It also found lower model performance on measured associative thinking and representational change, and difficulty selecting the most creative ideas. Those are findings for that study’s instruments and model versions, not a verdict on every kind of creative work.
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A large-scale divergent-creativity comparison published by Wang and colleagues in Nature Human Behaviour in 2026 involved 9,198 human participants and 215,542 observations from large language models. Its abstract reports slightly higher average performance among humans and more human variability, including stronger performance in the right-hand tail. The counts describe that study’s sample and observations; they are not a census of people and AI systems or a measure of creativity in every domain.
A 2025 meta-analysis by de Rooij and Biskjaer combined 17 studies and 115 effect sizes on creative idea generation. Its pooled estimate slightly favored ideas from prompted generative AI, but the confidence interval included zero and the studies were substantially heterogeneous. The analysis therefore did not find statistically significant overall evidence that AI had surpassed humans on the tested tasks. That is not proof that the two perform equally: a non-significant result does not establish equivalence.
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What does “creativity” measure?
Different measures capture different parts of creative work. A high score on a task is evidence about performance on that task; it does not, by itself, show that a system can carry out every thinking process involved in creativity or decide what matters in a real situation.
Ideas and outputs
Some assessments focus on the ideas or finished work produced: how novel, varied, or otherwise successful an answer appears under the test’s criteria. These measures can be useful for comparing output on a defined task. They cannot automatically answer whether the result fits a specific audience, communicates the intended meaning, or deserves to be chosen over other plausible options.
Thinking processes
Other assessments examine skills involved in reaching or changing ideas. The 2026 study described above distinguished its output results from measured processes such as associative thinking and representational change. Its mixed findings are a reminder that an answer that looks creative and the processes measured behind it are not interchangeable.
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Meaning and value
The UNDP’s Human Development Report 2025: A Matter of Choice: People and Possibilities in the Age of AI frames creativity through novelty, surprise, and value. It argues that AI may help with the first two, while people determine value. This is a conceptual argument in a policy report, not an experimental law about what AI can or cannot do. It highlights a practical distinction: an unexpected or impressive output still needs to be judged against human purposes and circumstances.
Where can generative AI help with creative work?
AI is most useful when a task benefits from quickly exploring alternatives and a person has the knowledge to evaluate them. A 2026 review by Caprioli in Current Opinion in Psychology describes potential gains in idea quantity and quality, speed, and cognitive load across creative fields including writing, design, and product ideation. These are potential benefits, not guaranteed results for every user or assignment.
Generating options
A system can contribute candidate directions when a person wants a range of possibilities to consider. This is different from asking it to decide which idea is best. The 2026 study’s finding that models had difficulty selecting the most creative ideas makes that distinction especially important: generation and evaluation are separate jobs, even within a single workflow.
Exploring a bounded task
Defined prompts and tasks make it easier to assess whether an answer meets a brief. The study results show that models can perform well on some bounded thinking measures, while the meta-analysis concerns prompted idea-generation tasks. Neither source establishes that success on a constrained exercise transfers to open-ended creative work, or that a strong result will be appropriate for a real project.
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The OECD’s education report, Reimagining Teaching in an Accelerating World, says generative AI can support the creative process and the acquisition of creative skills. That claim belongs in its education context. It is not evidence that AI improves every learner’s creativity, nor that the same effect occurs in every profession. The report also warns that similar prompts can produce similar suggestions and may reduce collective originality.
What are AI’s limits in creative work?
The evidence does not support treating a plausible or polished answer as proof that AI has handled the whole creative task. Performance can differ between making an output, demonstrating selected processes, and choosing an idea that is valuable in context.
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One task does not stand for every medium
The 2026 comparison of three model versions reported different outcomes across problem-solving and creative writing tasks. That contrast is a reason to evaluate the actual task at hand rather than generalize from a single score or demonstration. It does not establish that every model will perform the same way on every writing assignment.
Producing candidates is not the same as choosing well
In that same study, the models had difficulty selecting the most creative ideas. If a project produces many possibilities, the person still needs to decide which ones meet the brief, fit the audience, and merit further work. An abundance of suggestions can shift effort from coming up with ideas to comparing and refining them; whether that trade-off helps depends on the task and the user’s ability to evaluate the options.
Novelty alone is not enough
A surprising result may still be irrelevant or unsuitable. The UNDP report’s distinction between novelty, surprise, and value is useful here: deciding whether an output matters requires a standard tied to the people and purpose involved. That is a judgment to make explicitly, rather than inferring value from an answer’s fluency or unusualness.
Can using AI reduce originality or creative ownership?
It can create those risks, but the evidence does not make them inevitable. Caprioli’s 2026 review identifies possible costs including more homogenized ideas, diminished authenticity or ownership, reduced independent creative engagement, and weaker team collaboration. The review emphasizes that outcomes depend on when AI enters the process, how the interaction is designed, what the task requires, and the user’s expertise.
The OECD’s warning about similar prompts and similar suggestions is specifically part of its education discussion. In that setting, repeated reliance on similar suggestions may reduce collective originality. It should not be presented as proof that every group, profession, or creative task will experience the same effect.
A practical safeguard is to decide what you want to contribute before asking for suggestions. Write down your goal, constraints, or first ideas; then use AI to challenge or extend them. Finally, make your own selection and revision against criteria you can explain. This is a reasonable workflow recommendation informed by concerns about timing and independent engagement—not a proven best method for every person.
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When should you use AI, and when should you keep the work human-led?
Choose based on the job the work must do, not on a general claim that one side is more creative. The following framework turns the evidence’s task, timing, interaction, and expertise factors into practical questions.
| Decision factor | AI may be useful when… | Keep human judgment prominent when… |
|---|---|---|
| Task and definition of “good” | The work benefits from exploring alternatives, and the criteria for reviewing them are reasonably clear. | Success depends on context, values, a distinctive point of view, or consequences that are hard to capture in a prompt. |
| Timing | You want a system to expand or challenge ideas after you have defined your goal or initial direction. | Starting with outside suggestions could crowd out your own framing or independent exploration. |
| Interaction | The process leaves room for a person to select, revise, and justify what to use. | The workflow treats generated suggestions as final without meaningful human evaluation. |
| User expertise | You can recognize useful, weak, or unsuitable suggestions and improve the promising ones. | You lack the subject knowledge needed to judge whether an answer fits the situation. |
This is a decision aid, not a claim that AI cannot perform context-sensitive tasks or that people always judge better. It follows the moderator framework described in the 2026 review and the OECD’s 2025 discussion of task, user experience, trust, and human expertise.
Use AI as a contributor when
- You want candidates to react to, rather than an authority to settle the decision.
- The task benefits from exploring directions and you can define what a useful result would look like.
- You have enough expertise to select and revise suggestions rather than accepting them by default.
Keep human-led ideation or review prominent when
- The work depends on deeply situated knowledge or a personal point of view.
- Choosing well matters as much as generating possibilities.
- The decision carries responsibilities that require a person to assess meaning, appropriateness, or value.
How can you build AI into a creative workflow without handing over the judgment?
Use an explicit sequence that separates defining the work, generating alternatives, and evaluating them. The steps below are a practical recommendation, not a universally tested workflow.
- Set the human brief. State the purpose, intended audience, constraints, and what would make an answer useful. Note your own ideas or first direction before requesting suggestions.
- Choose a limited role for AI. Ask it to provide alternatives, explore a different angle, or challenge an idea—not to declare the winning option. Match the request to a part of the task you can assess.
- Review the candidates against your criteria. Separate a suggestion’s novelty from its relevance and value. Keep only ideas that serve the brief, and be willing to reject fluent or surprising ones.
- Revise with human intent. Develop the selected material so it reflects the project’s actual context and point of view. The point is not merely to retain an output, but to make deliberate choices about what it should become.
- Check the result as a whole. Ask whether the final work meets the purpose you set and whether you can explain why the chosen direction is appropriate. If not, return to the brief or generate a different set of options.
This sequence helps preserve a person’s role in framing and evaluation while allowing AI to contribute ideas. It does not remove the need for careful judgment, and it should be adjusted to the work rather than followed as a fixed formula.
Do findings in science, business, and education apply to all creative work?
No. The available evidence covers specific settings and cannot support one conclusion for every profession, medium, or future model. Pay attention to the scope of a finding before carrying it over to another context.
Scientific research
A 2025 European Commission Directorate-General for Research and Innovation working document, Artificial intelligence in science – Promises or perils for creativity?, examines 80 fields over 2000–2022. Its summary describes overall enhancement in measured novelty and impact, but also strong variation between fields, including areas with little improvement or negative effects. This is evidence about scientific research over that period, not a direct comparison of all human and AI creative work.
Business and innovation
The OECD’s 2025 review by Calvino, Reijerink, and Samek, The effects of generative AI on productivity, innovation and entrepreneurship, likewise says effectiveness depends on the task and user experience. It discusses trust and human expertise as well as evidence gaps about long-term business effects and workers’ understanding of AI limits. Those cautions matter when a promising use in one task is being considered for a broader business process.
Education
The OECD’s teaching report discusses support for creative processes and skill acquisition, alongside the risk that similar prompts yield similar suggestions and reduce collective originality. That framing is useful for teachers and learners, but its educational scope should remain visible when drawing conclusions. It does not establish identical effects in workplaces or in independent creative practice.
What should you take from the evidence?
Generative AI can contribute meaningfully to creative work, particularly when the task benefits from candidate ideas and a person can evaluate them. But task-specific success, a high-rated output, or increased novelty in a particular domain is not enough to establish that AI is generally more creative than people. The strongest practical approach is to define what “good” means for the work, choose deliberately when AI enters, and preserve human evaluation wherever meaning and value depend on context.
Frequently Asked Questions
Are the studies comparing the same AI models and creativity tests?
No. For example, the 2026 task comparison tested GPT-3.5-turbo, GPT-4, and GPT-4o, while other studies used different tasks and designs. Their results should be read as evidence about the models and measures each study examined, not as a single standardized contest.
Can results about AI and scientific creativity tell me whether AI improves fiction writing?
Not directly. The European Commission’s 2025 working document concerns measured novelty and impact in scientific research across 80 fields during 2000–2022; it is not a study of fiction or a general human-versus-AI writing comparison.
Does the meta-analysis prove that people and AI are equally creative?
No. Its pooled estimate was not statistically significant, but that does not prove equivalence. The authors also report substantial variation among the 17 included studies, so the result should not be treated as a universal tie.
Is the evidence settled for future versions of generative AI?
No. The cited comparisons concern particular versions, tasks, and periods, and creative output is not measured with one universal standard. Claims about newer systems need evidence from those systems and relevant tasks rather than extrapolation from these findings.
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